class_docstr: !
  @brief Class represents graph coloring algorithm based on hysteresis oscillatory network. 
         This is bio-inspired algorithm where the network uses relaxation oscillators that is
         regarded as a multi-vibrator. Each ensemble of synchronous oscillators corresponds to
         only one color.
  
  Example
  @code
      # import required modules
      from pyclustering.nnet.hysteresis import hysteresis_visualizer;
      
      from pyclustering.gcolor.hysteresis import hysteresisgcolor;
      
      from pyclustering.utils.graph import read_graph, draw_graph;
      
      # load graph from a file
      graph = read_graph(filename);
      
      # create oscillatory network for solving graph coloring problem
      network = hysteresisgcolor(graph.data, alpha, eps);
      
      # perform simulation of the network
      output_dynamic = network.simulate(2000, 20);
      
      # show dynamic of the network
      hysteresis_visualizer.show_output_dynamic(output_dynamic);
      
      # obtain results of graph coloring and display results
      coloring_map = hysteresis_visualizer.allocate_map_coloring();
      draw_graph(graph, coloring_map);
  @endcode
docstr: !
  @brief Peforms graph coloring analysis using simulation of the oscillatory network.
  
  @param[in] steps (uint): Number steps of simulations during simulation.
  @param[in] time (double): Time of simulation.
  @param[in] collect_dynamic (bool): Specified requirement to collect whole dynamic of the network.
  
  @return (hysteresis_analyser) Returns analyser of results of clustering.
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code: def process(self, steps, time, collect_dynamic=True):
      """!
      @brief Peforms graph coloring analysis using simulation of the oscillatory network.
  
      @param[in] steps (uint): Number steps of simulations during simulation.
      @param[in] time (double): Time of simulation.
      @param[in] collect_dynamic (bool): Specified requirement to collect whole dynamic of the network.
  
      @return (hysteresis_analyser) Returns analyser of results of clustering.
  
      """
  
      output_dynamic = super().simulate(
          steps, time, collect_dynamic=collect_dynamic
      )
      return hysteresis_analyser(
          output_dynamic.output, output_dynamic.time
      )
  
file_path: pyclustering/pyclustering/gcolor/hysteresis.py
imports: from pyclustering.nnet.hysteresis import hysteresis_network, hysteresis_dynamic
label_desc: Use image node_id 2 for calling the hysteresisgcolor obj's underlying member method code with example usage: obj.process(steps, time, collect_dynamic) and returns: hysteresis_analyser